应用人工智能来预测内血管动脉瘤修复后的并发症
Becky Long1, Danielle L Cremat1, Eduardo Serpa1
1Department of Surgery, College of Medicine, Central Michigan University, Saginaw, MI, USA.
Vascular and endovascular surgery
|July 10, 2023
概括
一个人工智能 (AI) 模型准确地预测了内血管动脉瘤修复 (EVAR) 后的术后并发症. 这种人工智能工具可以识别高风险患者进行密集监测,改善患者的治疗结果并优化后续护理.
科学领域:
- 血管外科 血管外科
- 人工智能在医学中的应用
- 医学成像分析 医学成像分析
背景情况:
- 内血管动脉瘤修复 (EVAR) 后的术后并发症可能危及生命.
- 目前的患者监测方法具有挑战性,患者随访率下降,特别是在第一年后.
- 需要预测模型来识别需要更密集的术后监测的患者.
研究的目的:
- 开发一种人工智能 (AI) 模型,以预测EVAR后个体患者的并发症概率.
- 确定那些可以从更密集的术后监测中受益的患者.
- 为优化对EVAR患者的监测策略.
主要方法:
- 一个深层卷积神经网络 (VascAI©) 是使用手术前CT血管造影 (CTA) 3D重建图像开发的,该图像来自273名接受EVAR的患者.
- 该模型利用3DCT图像来预测手术后并发症的风险.
- 采用数据减量采样和增量技术来解决数据不平衡并提高模型性能.
主要成果:
- 人工智能模型在识别EVAR后出现术后并发症的患者时实现了100%的灵敏度.
- 该模型正确识别了所有出现并发症的患者.
- 该模型显示高假阳性率 (44%),表明有可能减少大量患者的监测频率.
结论:
- 人工智能模型可以准确预测EVAR后手术后并发症的风险.
- 开发的AI模型只需要手术前的AAA CTA图像作为输入,消除了对专家注释数据的需求.
- 这种人工智能工具可以帮助识别EVAR并发症的高风险患者,指导监测合规性.
关键词:
在CT监控监控中,CT监控埃瓦尔·埃瓦尔 (Evar Evar) 是一个人.瓦斯卡莱©是一位人工智能的人工智能是人工智能.血管内腹部大动脉动脉瘤修复神经网络的神经网络的神经网络预测模型 预测模型更多相关视频
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